Capacity-achieving probability measure for conditionally Gaussian channels with bounded inputs |
| |
Authors: | Chan TH Hranilovic S Kschischang FR |
| |
Affiliation: | Dept. of Comput. Sci., Univ. of Regina, Sask., Canada; |
| |
Abstract: | A conditionally Gaussian channel is a vector channel in which the channel output, given the channel input, has a Gaussian distribution with (well-behaved) input-dependent mean and covariance. We study the capacity-achieving probability measure for conditionally Gaussian channels subject to bounded-input constraints and average cost constraints. Many practical communication systems, including additive Gaussian noise channels, certain optical channels, fading channels, and interference channels fall within this framework. Subject to bounded-input constraint (and average cost constraints), we show that the channel capacity is achievable and we derive a necessary and sufficient condition for a probability measure to be capacity achieving. Under certain conditions, the capacity-achieving measure is proved to be discrete. |
| |
Keywords: | |
|
|